The optical microlithography industry is permanently challenged by reduction of feature sizes. The significance of small defects in masks (also referred to as reticles, lithographic masks or photomasks) grows accordingly. The systematic deviations of features from the intended design become significant and should be measured with increasing accuracy. Therefore the ability to extract exact information of a photomask content in a precise, robust and fast manner is of increasing importance.
Mask inspection by aerial imaging is used to evaluate masks. The basic purpose is to conclude about mask contents by analysis of corresponding aerial image. Such analysis should result in precise information about patterns presented in the mask. For instance, critical dimension (CD) widths of patterns may have to be found. The most important part of the inspection is pattern defects detection. Any significant discrepancy of a pattern with respect to its desired form, which is stored in a database, should be detected.
Edge detection is a significant tool of the analysis. The task of edge detection is to recover a pattern shape (contour). Contours provide important information about pattern size (CD) and possible defects such as edge extrusion/intrusion, corner extrusion/intrusion etc.
Recent studies of CD of rectangular patterns (contacts) by simulations and machine inspections in partial coherent optical imaging have shown significant bias in CD evaluation by image edge detection with the help of standard edge detection techniques. Namely, in simulations the measured contact size were 20% smaller than given by the Data Base of the mask, while in machine the bias were much smaller ˜2.5%. The possible explanation for the bias in simulations was an assumption of incorrectness of current edge detection approaches for edge detection in optical imaging.
There is a growing need to provide more accurate manners to detect edges.